{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision tree accuracy: 0.688\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "X, y = make_classification(\n",
    "    n_samples=1000, n_features=50, n_informative=30, n_clusters_per_class=3, random_state=11)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=11)\n",
    "\n",
    "clf = DecisionTreeClassifier(random_state=11)\n",
    "clf.fit(X_train, y_train)\n",
    "print('Decision tree accuracy: %s' % clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f86a56bcdd0>]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DbawqzRtxWOfqkjzeONJOfyD2qnsPbjlIdmoSV1cUxzxWRPjEBWUcaO7m5f3N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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86a5200cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# When an argument for the base_estimator parameter is not passed, the default DecisionTreeClassifier is used\n",
    "clf = AdaBoostClassifier(n_estimators=50, random_state=11)\n",
    "clf.fit(X_train, y_train)\n",
    "accuracies.append(clf.score(X_test, y_test))\n",
    "\n",
    "plt.title('Ensemble Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Number of base estimators in ensemble')\n",
    "plt.plot(range(1, 51), [accuracy for accuracy in clf.staged_score(X_test, y_test)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
